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Hetzroni, A., Department of Agricultural Engineering, Purdue University, West Lafayette, IN 47907-1146, United States
Miles, G.E., Department of Agricultural Engineering, Purdue University, West Lafayette, IN 47907-1146, United States
Engel, B.A., Department of Agricultural Engineering, Purdue University, West Lafayette, IN 47907-1146, United States
Hammer, P.A., Department of Horticulture, Purdue University, West Lafayette, IN 47907-1146, United States
Latin, R.X., Department of Botany and Plant Pathology, Purdue University, West Lafayette, IN 47907-1146, United States
Techniques and algorithms to detect and diagnose disorders in plants grown in a controlled environment have been developed. A video camera senses features of plants which are inductive of disorders. Images are calibrated for size and color variations by using calibration templates. Different image segmentation techniques for separating object from background, have been implemented. Plant size and color properties have been investigated, temporal, spectral and spatial variation of leaves were extracted from the segmented images. Neural network and statistical classifiers were used to determine plant condition. © 1994.
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Machine vision monitoring of plant health
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Hetzroni, A., Department of Agricultural Engineering, Purdue University, West Lafayette, IN 47907-1146, United States
Miles, G.E., Department of Agricultural Engineering, Purdue University, West Lafayette, IN 47907-1146, United States
Engel, B.A., Department of Agricultural Engineering, Purdue University, West Lafayette, IN 47907-1146, United States
Hammer, P.A., Department of Horticulture, Purdue University, West Lafayette, IN 47907-1146, United States
Latin, R.X., Department of Botany and Plant Pathology, Purdue University, West Lafayette, IN 47907-1146, United States
Machine vision monitoring of plant health
Techniques and algorithms to detect and diagnose disorders in plants grown in a controlled environment have been developed. A video camera senses features of plants which are inductive of disorders. Images are calibrated for size and color variations by using calibration templates. Different image segmentation techniques for separating object from background, have been implemented. Plant size and color properties have been investigated, temporal, spectral and spatial variation of leaves were extracted from the segmented images. Neural network and statistical classifiers were used to determine plant condition. © 1994.
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